We are using the average baseball player’s salary from 2003 through 2020 to fit different auto-regressive models. The data is provided below: Year Salary ($mil) 2003 2.03 2004 2.29 2005 2.38 2006 2.58 2007 2.49 2008 2.63 2009 2.83 2010 2.92 2011 3.13 2012 3.26 2013 3.27 2014 3.32 2015 3.38 2016 3.62 2017 3.81 2018 4.25 2019 4.40 2020 5.15
Correlation
Correlation defines a relationship between two independent variables. It tells the degree to which variables move in relation to each other. When two sets of data are related to each other, there is a correlation between them.
Linear Correlation
A correlation is used to determine the relationships between numerical and categorical variables. In other words, it is an indicator of how things are connected to one another. The correlation analysis is the study of how variables are related.
Regression Analysis
Regression analysis is a statistical method in which it estimates the relationship between a dependent variable and one or more independent variable. In simple terms dependent variable is called as outcome variable and independent variable is called as predictors. Regression analysis is one of the methods to find the trends in data. The independent variable used in Regression analysis is named Predictor variable. It offers data of an associated dependent variable regarding a particular outcome.
We are using the average baseball player’s salary from 2003 through 2020 to fit different auto-regressive models. The data is provided below:
|
Year |
Salary ($mil) |
|
2003 |
2.03 |
|
2004 |
2.29 |
|
2005 |
2.38 |
|
2006 |
2.58 |
|
2007 |
2.49 |
|
2008 |
2.63 |
|
2009 |
2.83 |
|
2010 |
2.92 |
|
2011 |
3.13 |
|
2012 |
3.26 |
|
2013 |
3.27 |
|
2014 |
3.32 |
|
2015 |
3.38 |
|
2016 |
3.62 |
|
2017 |
3.81 |
|
2018 |
4.25 |
|
2019 |
4.40 |
|
2020 |
5.15 |
- fit a third-order autoregressive model to the average baseball salary and test for the significance of the third-order autoregressive parameter (slope). (Use alpha = 0.05.)
- if necessary, fit a second-order autoregressive model to the average baseball salary and test for the significance of the second-order autoregressive parameter (slope). (Use alpha = 0.05.)
- if necessary, fit a first-order autoregressive model to the average baseball salary and test for the significance of the first order autoregressive parameter (slope). (Use alpha = 0.05.)
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